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WSO Free Modeling Series - Now Open Through
May 31, 2024
9 Free Financial Modeling Lessons
Get instant access to lessons taught by experienced private equity pros and bulge bracket investment bankers including financial statement modeling, DCF, M&A, LBO, Comps and Excel Modeling.
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It’s hilarious. We have a massive model with myriad tabs, schedules, build-ups for everything. My MD gave buyers guidance around $Xmm. Nobody bid. Cuts the valuation range the next day ??? Why even have a model
Just to keep us busy, it’s basically just fake work lol. Shit I’m not complaining, make a $100k+ salary at 22-24 years old
This is an example of bad modeling. Many banks that can tell a compelling growth story gain credibility. Prime examples would be when the company being sold has multi-year contracts with their customers, essentially locking them in. As those customers scale their business with the company being sold, there is a transparent growth story.
Oftentimes we will use some variation of management / the banks financial forecast in our IOI model.
it's the illusion of work so that they can charge what they charge. they're rather pointless otherwise
They are a complete joke and I agree with the sentiments of everyone else, but I want to provide a bit of a counterpoint. As a buyer, I obviously don't trust or care about the assumptions/outcome, but it helps me understand the business. I can look at the model to get a sense of how the finances work: what drives revenue, what are the big cost items, are they variable or fixed, what are cash flow needs, etc. I know they're put together by analysts that don't know that much, but it's still helpful to level set how I should think about the business.
Also, I’m going to have to make one, and it’s much easier for me if I can rip a lot of it from the bank model.
I'm in PE and do think that models are extremely useful. Not because they are super useful in predicting the actual value of a company, but because they help you understand the value drivers in a deal. For example, we're currently heading into a downturn which will result in negative impact on revenue and profitability. However, this might allow us to purchase add-ons at a lower multiple in relation to their "normalized" earnings, which will actually benefit the returns even if we take a big hit next year organically. It also helps you see what you need to believe in to achieve a desired return and then you can conclude whether that development is actually likely operationally. However, I do totally agree that sell-side models are usually pointless. Can't even count how many times we have received IMs for companies that have grown 5-10% past years that are suddenly supposed to grow 20%+ per year and increase margins by 5-10 percentage points. Gets even more ridiculous as the assumptions are rarely backed up by anything that is even remotely reliable.
If you're using a model purely to generate a final number of how much a company should be valued, then you're using it wrong. The value of the model isn't in predicting the future - everyone knows the assumptions are basically worthless and nobody, least of all a fresh university graduate has any idea how a company might grow with any amount of accuracy.
What it's actually useful for is being able to adjust certain drivers and inputs and seeing how it might affect the valuation of the company. This way, you can easily calculate what happens to the business if revenues do increase by 10% next year, or if certain material costs end up decreasing by 5% next year, rather than just leaving it to estimation and having no idea how a certain scenario might actually affect the company. This is why all the best practices always point to ensuring the operating model is linked properly and you don't leave hardcodes everywhere - it's supposed to be more of a scenario analyzer rather than a crystal ball.
If you're sell-side, maybe yes. But on the buy-side things are different. For instance, just to give you an example, I'll take Howard Marks's thesis on risk (see below) which is one that I like. The graph, in a nutshell: The higher the potential return on an investment, the higher its risk deviation.
When you forecast 3 potential scenarios: Best, mediocre, and bad, you should take into account that all of those are possibilities and because two of them didn't end up happening it doesn't mean that they couldn't have happened (Fooled by Randomness is a great book about this topic). Ideally, a good financial model should allow you to put a number (%) on the possibility of different scenarios and calculate how much money you would be willing to invest to bear X% of risk for a Y% return.
Alternatively, if you forecast, let's say, 5 different scenarios (good, half-good, medium, half-bad, bad), now you're closer to predicting where your investment may go, and if you apply a second level of thinking, you can prepare preventively on how to react when the numbers take an undesired path. The more different scenarios you forecast, the greater the chance of nailing the scenario that ends up happening. Also, ideally, when you forecast the undesired scenarios, you should consider preventively what options may be available in that circumstances (e.g. if you forecast a "bad" scenario model with certain numbers, you could already know who would be interested to buy the asset at that price range). Some may say that all of those steps are too much, but I don't like uncertainty so always having a number prepared upfront and an exit strategy for A, B, C ... Z scenario allows me to sleep better.
Also, in private equity and real estate, forecasting may be even more relevant (compared to public markets) because certain aspects of your investments fall within your control (improving operations, cutting costs, acquisitions, etc.) or understanding (business cycles) so you can forecast some numbers based on the pre-planned decision on the business's financials or how you expect the economy to go.
Of course, I ain't saying that modeling is the ultimate decision-maker in investments, but it's just an instrument to support your investment decision. Not necessary, but could be helpful.
The whole idea of generating models with a significant range of output valuations is silly. The reason that models don't work is that growth rates aren't being created using a combination of historical growth + the type of business (low/med/high growth/cyclical/turnaround/asset play (see Lynch)) + qualitative factors (ala Phillip Fisher). If anything, you might have two or three models, one of which is the expected case, and the other two are based off the major risk factors to the business (TSMC = Geopolitical risk, Olaplex Holdings = Lack of demand, Clinical Stage Biopharma = Failure to execute). Each of the models should be provided a percentage of the valuation if the risk can be quantified, and if it can't, you'll just need to adopt a bit of a risk taker's perspective. You also shouldn't be DCFing unproven businesses with no track record. That way lies the road to ruin.
If you have those factors in your growth assumptions, you should be able to hit the nail on the head, from a valuation perspective.
Really, this isn't an impossible task from an (good, unbiased) ER or HF perspective, although M&A models are hopeless given the inherent conflicts of interest...
That's just my two cents.